import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import tree

from sklearn.tree import DecisionTreeClassifier

df = pd.read_csv('new.csv')
df.dropna()
df

from sklearn.preprocessing import OneHotEncoder

OHenc = OneHotEncoder()
df['Genre'] = OHenc.fit_transform(df[['Genre']]).toarray()
df

from sklearn.preprocessing import OrdinalEncoder
df= df.replace({'Publisher': np.nan}, 'Publisher')
Oenc = OrdinalEncoder()
df[['Name', 'Platform', 'Year', 'Publisher']] = Oenc.fit_transform(df[['Name', 'Platform', 'Year', 'Publisher']])
df

	Unnamed: 0	Rank	Name	Platform	Year	Genre	Publisher	NA_Sales	EU_Sales	JP_Sales	Other_Sales	Global_Sales
0	0	9424	511.0	11.0	13.0	0.0	114.0	0.00	0.00	0.13	0.00	0.13
1	1	15694	326.0	15.0	28.0	0.0	66.0	0.00	0.00	0.02	0.00	0.02
2	2	15166	64.0	9.0	24.0	0.0	121.0	0.00	0.02	0.00	0.00	0.02
3	3	12177	513.0	12.0	23.0	0.0	114.0	0.00	0.00	0.07	0.00	0.07
4	4	9874	105.0	5.0	17.0	0.0	80.0	0.08	0.03	0.00	0.00	0.12
...	...	...	...	...	...	...	...	...	...	...	...	...
548	548	12799	393.0	3.0	21.0	0.0	20.0	0.05	0.00	0.00	0.00	0.06
549	549	14424	65.0	9.0	22.0	0.0	11.0	0.00	0.02	0.00	0.01	0.03
550	550	7549	222.0	14.0	23.0	0.0	104.0	0.19	0.00	0.00	0.02	0.20
551	551	2207	461.0	20.0	25.0	1.0	86.0	0.48	0.36	0.02	0.08	0.94
552	552	15733	420.0	3.0	22.0	0.0	79.0	0.02	0.00	0.00	0.00	0.02

553 rows × 12 columns

from sklearn.model_selection import train_test_split

Y = df['Platform']
X = df.drop(columns='Platform')

X_train, X_test, Y_train, Y_test = train_test_split(X,Y, 
                                                    test_size=0.2, 
                                                    random_state=42)
                                                    

x_train = X_train.dropna()
y_train = Y_train[x_train.index]
x_test = X_test.dropna()
y_test = Y_test[x_test.index]

#with
dtc = DecisionTreeClassifier(max_depth=5,
                                  min_samples_leaf=4,
                                  max_leaf_nodes=200)
dtc = dtc.fit(X_train.iloc[::1], Y_train.iloc[::1])
plt.figure(figsize=(70,70))
tree.plot_tree(dtc, filled=True, fontsize=10)

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#without
dtc_without = DecisionTreeClassifier()
dtc_without = dtc_without.fit(X_train.iloc[::1], Y_train.iloc[::1])
plt.figure(figsize=(70,70))
tree.plot_tree(dtc_without, filled=True, fontsize=10)

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from sklearn import metrics

Y_predicts = dtc.predict(X_test)
Y_predicts_W = dtc_without.predict(X_test)

con_mat = metrics.confusion_matrix(Y_test, Y_predicts)
con_mat

array([[ 0,  0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,
         0,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  2,  0,  0,
         0,  0,  0,  0],
       [ 0,  0, 10,  0,  1,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  1,
         0,  0,  0,  0],
       [ 0,  0,  0,  0,  5,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  4],
       [ 0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  2,
         0,  0,  0,  0],
       [ 0,  0,  5,  0,  0,  0,  0,  0,  1,  1,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0,  0,  7,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  5,  0,  2,  3,  0,  0,  0,  6,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  3,  0,  0,  0,  0,  0,  0,  0,  3,  0,  0,  1,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  2,  2,  0,  1,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  5,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  2,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  2,
         0,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  6,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  3,  0,  0,  0,  0,  0,  0,  0,  2,  2,  0,  1,  0,  0,
         0,  0,  0,  0],
       [ 0,  0,  0,  0,  6,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  3]], dtype=int64)

con_mat_w = metrics.confusion_matrix(Y_test, Y_predicts_W)
con_mat_w

array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 1, 0, 5, 0, 0, 0, 0, 2, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0],
       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 2, 1, 0, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0],
       [0, 0, 0, 1, 0, 0, 0, 0, 3, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 2, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3],
       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 1, 0, 0, 0, 0, 0, 1, 1, 0],
       [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 1, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 1, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 5, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 2, 0, 2, 0],
       [0, 0, 0, 0, 0, 0, 2, 0, 2, 1, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2]],
      dtype=int64)

sns.heatmap(con_mat, annot=True, fmt="d")
plt.xlabel("Предсказания")
plt.ylabel("Реально")

Text(50.722222222222214, 0.5, 'Реально')

sns.heatmap(con_mat_w, annot=True, fmt="d")
plt.xlabel("Предсказания")
plt.ylabel("Реально")

Text(50.722222222222214, 0.5, 'Реально')

print("Точность", metrics.accuracy_score(Y_test, Y_predicts))
print("Чувствительность", metrics.recall_score(Y_test, Y_predicts, average='macro'))
print("Аккуратность", metrics.precision_score(Y_test, Y_predicts, average='macro'))
print("Финальная", metrics.f1_score(Y_test, Y_predicts, average='macro'))

Точность 0.3333333333333333
Чувствительность 0.21684523809523806
Аккуратность 0.16716575091575092
Финальная 0.17340359477124184

c:\Users\Alexander\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
c:\Users\Alexander\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

print("Точность", metrics.accuracy_score(Y_test, Y_predicts_W))
print("Чувствительность", metrics.recall_score(Y_test, Y_predicts_W, average='macro'))
print("Аккуратность", metrics.precision_score(Y_test, Y_predicts_W, average='macro'))
print("Финальная", metrics.f1_score(Y_test, Y_predicts_W, average='macro'))

Точность 0.36936936936936937
Чувствительность 0.3079081632653062
Аккуратность 0.3548455512741227
Финальная 0.3135931236626687

c:\Users\Alexander\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
c:\Users\Alexander\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))